Spaces:
Sleeping
A newer version of the Streamlit SDK is available:
1.36.0
title: CapiPort
emoji: 📈
sdk: streamlit
sdk_version: 1.32.0
app_file: main.py
pinned: false
license: mit
CapiPort V2
Overview
Welcome to our project on portfolio management for Indian equity markets! This project aims to help individuals efficiently allocate their money between different equities, optimizing returns while managing risk.
Features
- Dynamic Allocation: Our technique dynamically allocates funds among various equities based on a robust methodology.
- Risk Management: The project incorporates risk management strategies to enhance overall portfolio stability.
- User-Friendly Interface: Access the tool through our user-friendly web interface here.
Getting Started
Follow these steps to get started with the project:
Clone the repository:
git clone https://github.com/bhanuprasanna527/CapiPort/Install dependencies:
pip install -r requirements.txtRun the project:
python main.py
Technique used (Version 2)
Efficient Frontier
Parameters used:
1.1) Maximum Sharpe Ratio
1.2) Efficient Risk
1.3) Efficient Return
1.4) Minimum Volatility
Hierarchical Risk Parity
Overview
Implementation
Input: NSE Stock Tickers, Date you want to track from, Optimization Technique, Parameter to base on, and the amount you want to invest.
Objective: Maximize expected return while minimizing portfolio variance.
Optimization: Utilize an Optimization method to find the Optimal Allocation to maximize Returns and minimize Volatility. approach, adjusting weights to find the optimal allocation.
Output: The final set of weights representing the optimal portfolio allocation, Annual Returns, Cumulative Annual Returns, Cumulative Monthly Returns, and Monthly returns. You will also be able to see Visual charts for the analysis.
Contributing
We welcome contributions! If you have any improvement ideas, please feel free to open an issue or submit a pull request. License
This project is licensed under the MIT License.